QGIS Planet

How do you objectively define and compute which parts of a network are in the center? One approach is to use the concept of centrality.

Centrality refers to indicators which identify the most important vertices within a graph. Applications include identifying the most influential person(s) in a social network, key infrastructure nodes in the Internet or urban networks, and super spreaders of disease. (Source: http://en.wikipedia.org/wiki/Centrality)

Researching this topic, it turns out that some centrality measures have already been implemented in GRASS GIS. thumbs up!

In my opinion, the most interesting centrality measures for this street network are closeness and betweenness:

Closeness “measures to which extent a node i is near to all the other nodes along the shortest paths”. Closeness values are lowest in the center of the network and higher in the outskirts.

Betweenness “is based on the idea that a node is central if it lies between many other nodes, in the sense that it is traversed by many of the shortest paths connecting couples of nodes.” Betweenness values are highest on bridges and other important arterials while they are lowest for dead-end streets.

Correct turn restriction information is essential for the vehicle routing quality of any street network dataset – open or commercial. One of the challenges of this kind of information is that these restrictions are typically not directly visible on each map.

This post is inspired by a share on G+ which resurfaced in my notifications. In a post on the Mapbox blog, John Firebaugh presents the OSM iD editor which should make editing turn restrictions straight-forward: clicking on the source link turns the associated turn information visible. By clicking on the turn arrows, the user can easily toggle between allowed and forbidden.

But the issue of identifying wrong turn restrictions remains. One approach to solving this issue is to compare restriction information in OSM with the information in a reference data set.

This is possible by comparing routes computed on OSM and the reference data using a method I presented at FOSS4G (video): a turn restriction basically is a forbidden combination of links. If we compute the route from the start link of the forbidden combination to the end link, we can check if the resulting route geometry violates the restriction or uses an appropriate detour:

illustrative slide from my LBS2014 presentation on OSM vehicle routing quality – read more about this method and results for Vienna in our TGIS paper or the open pre-print version

It would be great to have an automated system comparing OSM and open government street network data to detect these differences. The quality of both data sets could benefit enormously by bundling their QA efforts. Unfortunately, the open government street network data sets I’m aware of don’t contain turn information.

In my opinion, Stamen’s Toner-lite map is one of the best background maps to use together with colorful overlays. The only downsides of using it in QGIS are that the OpenLayers plugin can not provide the tiles at print resolution and that the projection is limited to Web Mercator. That’s why I’ve started to recreate the style for OSM Spatialite databases:

So far, there are styles for lines and polygons and they work quite well for the scale range between 1:1 and 1:250000. As always, you can download the styles from QGIS-resources on Github.

Using OSM data in QGIS is a hot topic but so far, no best practices for downloading, preprocessing and styling the data have been established. There are many potential solutions with all their advantages and disadvantages. To give you a place to start, I thought I’d share a workflow which works for me to create maps like the following one from nothing but OSM:

Finishing preprocessing in QGIS

In QGIS, we’ll want to load the points, lines, and multipolygons using Add SpatiaLite Layer:

When we load the spatialite tables, there are a lot of features and some issues:

There is no land polygon. Instead, there are “coastline” line features.

Most river polygons are missing. Instead there are “riverbank” line features.

Luckily, creating the missing river polygons is not a big deal:

First, we need to select all the lines where waterway=riverbank.

Then, we can use the Polygonize tool from the processing toolbox to automatically create polygons from the areas enclosed by the selected riverbank lines. (Note that Processing by default operates only on the selected features but this setting can be changed in the Processing settings.)

Creating the land polygon (or sea polygon if you prefer that for some reason) is a little more involved since most of the time the coastline will not be closed for the simple reason that we are often cutting a piece of land out of the main continent. Therefore, before we can use the Polygonize tools, we have to close the area. To do that, I suggest to first select the coastline using "other_tags" LIKE '%"natural"=>"coastline"%' and create a new layer from this selection (save selection as …) and edit it (don’t forget to enable snapping!) to add lines to close the area. Then polygonize.

Extracting POIs from OpenStreetMap is reasonably simple using Overpass API. A very convenient way to construct the query is to use a query builder which allows you to select the area of interest and builds queries for different servers.

Of course you can fine-tune the query further. For example, you can add multiple key-value pairs to the query. I used the following query to select all Billa supermarkets:

Note the * in the query? It means that I’m querying all kinds of features: nodes, ways, and relations.

Save the server response to a .osm file. This file can be loaded into QGIS using simple drag-and-drop or Add Vector Layer. A dialog will open where you can select the type of features you want to load from the file. You can simply use Select All and OK to load everything.

My supermarket POIs came in two types: points and multipolygons. To style them both with nice supermarket SVG icons, I decided to use a Centroid fill with the SVG marker for the polygon layer:

OSM place search and osmSearch are two plugins for QGIS which use the Nominatim service to find addresses and places. They are both still marked as “experimental” plugins, so users are expected to expect the unexpected.

Once installed, both plugins look very similar: There is an input text field and a results list.

A simple search with street name and house number returns the expected results. Interacting with the result shows some differences:

OSM place search will highlight the location when you mouse-over the result in the list. On double-click, it will zoom to the result.

osmSearch will highlight the result and move the map center to the result if you double-click but won’t zoom.

Both plugins can deal with umlauts (ä,ö,ü) but only osmSearch works with háčeks.

A nice feature of osmSearch is that it remembers your previous searches and offers an auto-complete function.

OSM place search on the other hand offers a reverse “Where am I” function (the arrow pointing to the left” which tries to find a name for the current map center location. Additionally, there are functions to add the current object as a new layer or mask layer.

Both plugins have strong and weak points. Combined, they would make a really strong tool but then nothing prevents us from having them both and choosing the best one for the task at hand.

In my previous post, I presented a Processing model to determine positional accuracy of street networks. Today, I’ll cover another very popular tool to assess OSM quality in a region: network length comparison. Here’s the corresponding slide from my FOSS4G presentation which shows an example of this approach applied to OSM and OS data in the UK:

One building block of this tool is the Total graph length model which calculates the length of a network within specified regions. Like the model for positional accuracy, this model includes reprojection steps to ensure all layers are in the same CRS before the actual geoprocessing starts:

The final Compare total graph length model combines two instances of “Total graph length” whose results are then joined to eventually calculate the length difference (lenDIFF).

As usual, you can find the models on Github. If you have any questions, don’t hesitate to ask in the comments and if you find any issues please report them on Github.

Since most users probably don’t have access to QGIS 1.8 anymore, I’ll be updating the tools to QGIS 2.0 Processing. I’m starting today with the positional accuracy comparison tool. It is based on a method described by Goodchild & Hunter (1997). Here’s the corresponding slide from my FOSS4G presentation:

The basic idea is to evaluate the positional accuracy of a street graph by comparing it with a reference graph. To do that, we check how much of the graph lies within a certain tolerance (buffer) of the reference graph.

The processing model uses the following input: the two street graphs which should be compared, the size of the buffer (tolerance for positional accuracy), a polygon layer with analysis regions, and the field containing the region id. This is how the model looks in Processing modeler:

First, all layers are reprojected into a common CRS. This will have to be adjusted if the tool is used in other geographic regions. Then the reference graph is buffered and – since I found that dissolving buffers directly in the buffer tool can become very slow with big datasets – the faster difference tool is used to dissolve the buffers before we calculate the graph length inside the buffer (inbufLEN) as well as the total graph length in the analysis region (totalLEN). Finally, the two results are joined based on the region id field and the percentage of graph length within the buffered reference graph (inbufPERC) is calculated. A high percentage shows that both graphs agree very well geometrically.

The following image shows the tool applied to a sample of OpenStreetMap (red) and official data published by the city of Vienna (purple) at Wien Handelskai. OSM was used as a reference graph and the buffer size was set to 10 meters.

In general, both graphs agree quite well. The percentage of the official graph within 10 meters of the OSM graph is 93% in the 20th district. In the above image, we can see that links available in OSM are not contained in the official graph (mostly pedestrian/bike links) and there seem to be some connectivity issues as well in the upper right corner of the image.

In my opinion, Processing models are a great solution to document geoprocessing work flows and share them with others. If you want to collaborate on building more models for OSM-related analysis, just leave a comment bellow.

For some time now I’ve been using the BAG buildings in QGIS as an alternative to the Top10NL buildings. The BAG buildings are more accurate, after all. This is not to disqualify the Top10NL, on the contrary: each geo data set has its own merits. The Top10NL is unbeatable regarding terrain details. As for the […]

If you even visited my lovely home town of Swellendam in the Western Cape of South Africa on http://openstreetmap.org, you might have noticed that the building footprints for the town are almost non-existent. Building footprints provide a valuable way to understand impacts of flood and other natural hazards, as well as being a valuable source... Read more »

The default look of OSM plugin layers is not well-suited for background map usage. It’s too colorful and any overlay just gets lost:

Default look of OSM plugin layers

A good background map has enough detail to allow the reader to orient himself but at the same time must not overwhelm the overlays. For data to be overlaid onto OSM plugin layers, a more neutral style is needed. And this is my first attempt at such a style, containing rules for the polygon and line layers (no point style yet):

Light OSM style applied to polygon and line layer

As before, these styles can be downloaded from my “QGIS-resources” Github repository. They are called osm_light_line.qml and osm_light_polygon.qml and require QGIS trunk. They do not work in QGIS 1.7.x because they contain some new features that are only available in the developer version (aka trunk).

you should find a folder with the name of the prefix you chose inside the osm2po folder. It contains a log file which in turn provides a command line template for importing the OSM network into PostGIS, e.g.

This post covers the necessary steps to use osm2po on Windows. Osm2po is both, a converter and a routing engine. It parses OpenStreetMap data and makes it routable. While osm2pgrouting seems to be limited by the amount of memory that is available on your system, osm2po is able to convert large sets like europe.osm. It generates SQL Insert scripts for PostGIS, compatible with pgRouting and Quantum GIS.

To get started, download osm2po and an OSM binary file like e.g. austria.osm.pbf. Extract osm2po and you’ll find the .jar file together with the tool’s documentation (German only though).

Styling OSM road data is a bit tricky due to the many different possible options in OSM “type” tag. It takes some fiddling around to get results similar to Google. The easiest way to create such a style is using rule-based renderer with “Symbol levels” enabled.

I’ve excluded a number of road types from being rendered and labeled. This is done by NOT specifying a rule that fits those classes. That’s the reason why I don’t have a “no filter” rule. Instead, I’ve specified

type NOT IN ('footway','footpath','steps','cycleway','pedestrian','track','bridleway')

to cover all the roads I don’t want specifically highlighted but displayed using default style. This way I can make sure that footways and similar are neither drawn nor labeled.

Google-style rules for OSM with multiple zoom levels

Now, we can have a look at how to create those road shields Google uses:

Sample from Google Maps

Currently, it’s not possible to solve this using the labeling engine. And even after labels with “shields” will be implemented, there is still the problem that we want both road names and road numbers labeled – preferably without having to duplicate the layer.

For now, one possible solution for this is to create the shields using the “Marker lines” feature of new symbology. As the name suggests, you can put markers on lines. In this case, the marker will be our road shield. It basically has two parts: the colored shield plus the text. The shield can be created by putting two squares besides each other. (Adjust the offsets to move them besides each other.) Then we can put the text on top, one letter at a time.

Creating the road shield marker

Place the marker on the line once and deactivate “Rotate marker”.

A "road with shield" style

These styles can be assigned to every road you want to decorate with a shield. If you save the style, you just need to change the text next time.

And this is how the result can look like.

Zoomed-out result

Maybe the shields turned out a little too big compared with the original.

Zoomed in, more labels will be displayed and an additional layer with metro stations becomes visible. The metro symbol was created using the same technique described for the road shields.

Zoomed-in result

One disadvantage of creating road shields with this technique – besides the fact that it’s rather tedious – is that there is no collision detection between labels and shields. Nonetheless, it’s a viable solution that allows you to create high-resolution maps that look very similar to Google Maps using free OSM data.

Advanced labeling in QGIS new labeling engine is mostly about data-defined settings. Almost any property of the label can be controlled.

For this example, we will try to mimic the look of the classic Google map with it’s line and label styles. The data for this post is from the OpenStreetMap project provided as Shapefiles by Cloudmade.

After importing the roads into PostGIS using PostGIS Manager Plugin, we can create a view that will contain the necessary label style information. The trick here is to use CASE statements to distinguish between different label “classes”. Motorway labels will be bigger than the rest and the buffer color will be the same color as used for the corresponding lines.

drop view if exists v_osm_roads_styled ;
CREATE VIEW v_osm_roads_styled AS
SELECT *,
CASE WHEN type = 'motorway' THEN 9
ELSE 8 END
as font_size,
'black'::TEXT as font_color,
false as font_bold,
false as font_italic,
false as font_underline,
false as font_strikeout,
false as font_family,
1 as buffer_size,
CASE WHEN type = 'motorway' THEN '#fb9139'::TEXT
WHEN type IN ( 'primary','primary_link','secondary','secondary_link') THEN '#fffb8b'::TEXT
ELSE 'white'::TEXT END
as buffer_color
from osm_roads;

In QGIS, we can then load the view and start styling. First, let’s get the line style ready. Using rule-based renderer, it’s easy to create complex styles. In this case, I’ve left it rather simple and don’t distinguish between different zoom levels. That’s a topic for another post :)

Google-style rules for OSM road data

Now for the labels! In “Data defined settings”, we can assign the special attributes created in the database view to the settings.

Completed "Data defined settings"

To achieve an even better look, go to “Advanced” tab and enable “curved” and “on line” placement. “Merge connected lines to avoid duplicate labels” option is very helpful too.

Finally – after adding some water objects (Cloudmade natural.shp) – this is what our result looks like:

Google-style OSM map

This solution can be improved considerably by adding multiple zoom levels with corresponding styles. One obvious difference between the original Google map and this look-alike is the lack of road numbers. Tim’s post on “shield labels” can be a starting point for adding road numbers the way Google does.

This is something I have been wanting to do for a long time: map which areas of Vienna have fast access to a certain kind of infrastructure. Now, I finally found time and data to perform this analysis. Data used is OSM road data (Cloudmade shapefile) for Austria and metro station coordinates for Vienna by Max Kossatz and Robert Harm.

Before importing the OSM roads into PostGIS, I cut out my area of interest and created a clean topology using GRASS v.clean.break. Once loaded into the database, assign_vertex_id() function does the rest and the network is ready for routing and distance calculations.
For the metro stations, I calculated the nearest network node using George MacKerron’s Nearest Neighbor function.

Catchments were calculated using driving_distance() function. It returns distance to a given metro station for all network nodes (up to a maximum distance). The result can be interpolated to show e.g. which areas are at most 1 km away from any metro station.

1 km catchments around metro stations in Vienna

Close-up look at the 1 km catchment zone border

Once set up, performing this analysis is reasonably fast. Instead of metro stations, any other infrastructure coverage can be analyzed easily. I could imagine this being really useful when looking for a new flat: “Find me an area close to work, a metro station and a highschool.”

The next great thing would be to have all data for calculation of transit travel times too. Yes, I’m looking at you Wiener Linien!